{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "from keras.models import Sequential\n",
    "from keras.models import Model\n",
    "from keras.layers import Dropout, Flatten, Dense, Activation, Reshape, LeakyReLU\n",
    "from keras.callbacks import CSVLogger\n",
    "import tensorflow as tf\n",
    "from scipy.ndimage import imread\n",
    "import numpy as np\n",
    "import random\n",
    "from keras.layers import LSTM, GRU\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras import backend as K\n",
    "import keras\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import optimizers\n",
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import urllib2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'https://poloniex.com/public?command=returnChartData&currencyPair=USDT_BTC&start=1514561100&end=9999999999&period=300'\n",
    "openUrl = urllib2.urlopen(url)\n",
    "r = openUrl.read()\n",
    "openUrl.close()\n",
    "d = json.loads(r.decode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14300.000000</td>\n",
       "      <td>1514561100</td>\n",
       "      <td>14320.000000</td>\n",
       "      <td>14170.015912</td>\n",
       "      <td>14244.748167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14320.589161</td>\n",
       "      <td>1514561400</td>\n",
       "      <td>14380.494000</td>\n",
       "      <td>14258.000000</td>\n",
       "      <td>14300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14405.676265</td>\n",
       "      <td>1514561700</td>\n",
       "      <td>14459.160000</td>\n",
       "      <td>14320.589161</td>\n",
       "      <td>14320.589161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14489.000000</td>\n",
       "      <td>1514562000</td>\n",
       "      <td>14489.000000</td>\n",
       "      <td>14360.000000</td>\n",
       "      <td>14430.242615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14500.000000</td>\n",
       "      <td>1514562300</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>14469.234730</td>\n",
       "      <td>14489.168781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14441.093396</td>\n",
       "      <td>1514562600</td>\n",
       "      <td>14526.800298</td>\n",
       "      <td>14410.000000</td>\n",
       "      <td>14500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14484.211019</td>\n",
       "      <td>1514562900</td>\n",
       "      <td>14499.000001</td>\n",
       "      <td>14345.774573</td>\n",
       "      <td>14441.093396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14408.521121</td>\n",
       "      <td>1514563200</td>\n",
       "      <td>14499.000000</td>\n",
       "      <td>14260.000000</td>\n",
       "      <td>14441.982752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14430.000000</td>\n",
       "      <td>1514563500</td>\n",
       "      <td>14457.000000</td>\n",
       "      <td>14320.000000</td>\n",
       "      <td>14408.521121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14550.000000</td>\n",
       "      <td>1514563800</td>\n",
       "      <td>14550.000000</td>\n",
       "      <td>14405.000000</td>\n",
       "      <td>14430.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14600.000000</td>\n",
       "      <td>1514564100</td>\n",
       "      <td>14630.833638</td>\n",
       "      <td>14503.022631</td>\n",
       "      <td>14503.022631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14561.000000</td>\n",
       "      <td>1514564400</td>\n",
       "      <td>14601.000000</td>\n",
       "      <td>14550.000000</td>\n",
       "      <td>14600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14617.541440</td>\n",
       "      <td>1514564700</td>\n",
       "      <td>14641.112299</td>\n",
       "      <td>14529.640311</td>\n",
       "      <td>14575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14650.000000</td>\n",
       "      <td>1514565000</td>\n",
       "      <td>14692.557763</td>\n",
       "      <td>14617.541440</td>\n",
       "      <td>14617.541440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14722.552314</td>\n",
       "      <td>1514565300</td>\n",
       "      <td>14772.397869</td>\n",
       "      <td>14650.000000</td>\n",
       "      <td>14660.172648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14700.000000</td>\n",
       "      <td>1514565600</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>14679.093627</td>\n",
       "      <td>14722.552314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14662.000000</td>\n",
       "      <td>1514565900</td>\n",
       "      <td>14720.000000</td>\n",
       "      <td>14626.561050</td>\n",
       "      <td>14700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14581.000001</td>\n",
       "      <td>1514566200</td>\n",
       "      <td>14662.000000</td>\n",
       "      <td>14508.841627</td>\n",
       "      <td>14637.113082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14511.009300</td>\n",
       "      <td>1514566500</td>\n",
       "      <td>14596.000000</td>\n",
       "      <td>14469.130536</td>\n",
       "      <td>14581.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14460.000000</td>\n",
       "      <td>1514566800</td>\n",
       "      <td>14612.000000</td>\n",
       "      <td>14421.000000</td>\n",
       "      <td>14511.009300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14568.023391</td>\n",
       "      <td>1514567100</td>\n",
       "      <td>14650.000000</td>\n",
       "      <td>14460.000000</td>\n",
       "      <td>14480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14580.559876</td>\n",
       "      <td>1514567400</td>\n",
       "      <td>14665.000000</td>\n",
       "      <td>14550.000000</td>\n",
       "      <td>14597.559876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14557.000000</td>\n",
       "      <td>1514567700</td>\n",
       "      <td>14610.000000</td>\n",
       "      <td>14550.000000</td>\n",
       "      <td>14590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14631.229399</td>\n",
       "      <td>1514568000</td>\n",
       "      <td>14639.000000</td>\n",
       "      <td>14557.000001</td>\n",
       "      <td>14557.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14650.000000</td>\n",
       "      <td>1514568300</td>\n",
       "      <td>14660.000000</td>\n",
       "      <td>14597.559877</td>\n",
       "      <td>14631.229400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14646.078294</td>\n",
       "      <td>1514568600</td>\n",
       "      <td>14699.000000</td>\n",
       "      <td>14600.000000</td>\n",
       "      <td>14660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14699.966941</td>\n",
       "      <td>1514568900</td>\n",
       "      <td>14702.509015</td>\n",
       "      <td>14616.278866</td>\n",
       "      <td>14646.078294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14622.040735</td>\n",
       "      <td>1514569200</td>\n",
       "      <td>14699.966941</td>\n",
       "      <td>14600.000000</td>\n",
       "      <td>14699.966941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14570.000000</td>\n",
       "      <td>1514569500</td>\n",
       "      <td>14650.899897</td>\n",
       "      <td>14515.406246</td>\n",
       "      <td>14622.040735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14470.000000</td>\n",
       "      <td>1514569800</td>\n",
       "      <td>14570.000000</td>\n",
       "      <td>14431.108000</td>\n",
       "      <td>14570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>14325.772990</td>\n",
       "      <td>1514582100</td>\n",
       "      <td>14403.708980</td>\n",
       "      <td>14271.000000</td>\n",
       "      <td>14317.375141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>14300.010000</td>\n",
       "      <td>1514582400</td>\n",
       "      <td>14374.515000</td>\n",
       "      <td>14281.655035</td>\n",
       "      <td>14325.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>14214.000000</td>\n",
       "      <td>1514582700</td>\n",
       "      <td>14300.010000</td>\n",
       "      <td>14210.000000</td>\n",
       "      <td>14300.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>14165.388898</td>\n",
       "      <td>1514583000</td>\n",
       "      <td>14241.010723</td>\n",
       "      <td>14164.388898</td>\n",
       "      <td>14210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>14200.000000</td>\n",
       "      <td>1514583300</td>\n",
       "      <td>14238.548702</td>\n",
       "      <td>14165.388898</td>\n",
       "      <td>14183.007201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>14181.000000</td>\n",
       "      <td>1514583600</td>\n",
       "      <td>14256.249000</td>\n",
       "      <td>14158.120000</td>\n",
       "      <td>14200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>14001.000001</td>\n",
       "      <td>1514583900</td>\n",
       "      <td>14200.000000</td>\n",
       "      <td>13976.498067</td>\n",
       "      <td>14181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>14129.999999</td>\n",
       "      <td>1514584200</td>\n",
       "      <td>14172.000000</td>\n",
       "      <td>13976.237000</td>\n",
       "      <td>14001.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>14180.000000</td>\n",
       "      <td>1514584500</td>\n",
       "      <td>14200.000000</td>\n",
       "      <td>14051.437045</td>\n",
       "      <td>14129.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>14104.125000</td>\n",
       "      <td>1514584800</td>\n",
       "      <td>14206.747499</td>\n",
       "      <td>14104.125000</td>\n",
       "      <td>14180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>14155.000000</td>\n",
       "      <td>1514585100</td>\n",
       "      <td>14198.425843</td>\n",
       "      <td>14100.541500</td>\n",
       "      <td>14165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>14216.178247</td>\n",
       "      <td>1514585400</td>\n",
       "      <td>14282.235000</td>\n",
       "      <td>14131.000003</td>\n",
       "      <td>14155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>14241.601278</td>\n",
       "      <td>1514585700</td>\n",
       "      <td>14282.520000</td>\n",
       "      <td>14141.000000</td>\n",
       "      <td>14201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>14234.548783</td>\n",
       "      <td>1514586000</td>\n",
       "      <td>14289.548783</td>\n",
       "      <td>14200.020001</td>\n",
       "      <td>14241.601278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>14288.865950</td>\n",
       "      <td>1514586300</td>\n",
       "      <td>14329.191960</td>\n",
       "      <td>14234.558783</td>\n",
       "      <td>14270.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>14294.706193</td>\n",
       "      <td>1514586600</td>\n",
       "      <td>14320.000000</td>\n",
       "      <td>14227.567240</td>\n",
       "      <td>14319.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>14326.278692</td>\n",
       "      <td>1514586900</td>\n",
       "      <td>14332.560000</td>\n",
       "      <td>14192.721515</td>\n",
       "      <td>14294.706193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>14278.999900</td>\n",
       "      <td>1514587200</td>\n",
       "      <td>14326.278692</td>\n",
       "      <td>14264.090630</td>\n",
       "      <td>14326.278692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>14265.001000</td>\n",
       "      <td>1514587500</td>\n",
       "      <td>14313.379294</td>\n",
       "      <td>14265.001000</td>\n",
       "      <td>14278.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>14300.000001</td>\n",
       "      <td>1514587800</td>\n",
       "      <td>14340.367118</td>\n",
       "      <td>14233.644840</td>\n",
       "      <td>14265.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>14352.690000</td>\n",
       "      <td>1514588100</td>\n",
       "      <td>14365.000000</td>\n",
       "      <td>14300.000000</td>\n",
       "      <td>14300.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>14379.000000</td>\n",
       "      <td>1514588400</td>\n",
       "      <td>14382.010643</td>\n",
       "      <td>14352.690000</td>\n",
       "      <td>14352.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>14400.000000</td>\n",
       "      <td>1514588700</td>\n",
       "      <td>14400.000000</td>\n",
       "      <td>14365.000000</td>\n",
       "      <td>14378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>14490.000001</td>\n",
       "      <td>1514589000</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>14375.000000</td>\n",
       "      <td>14400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>14500.000000</td>\n",
       "      <td>1514589300</td>\n",
       "      <td>14525.000000</td>\n",
       "      <td>14400.000000</td>\n",
       "      <td>14465.664015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>14517.361256</td>\n",
       "      <td>1514589600</td>\n",
       "      <td>14560.000000</td>\n",
       "      <td>14486.374908</td>\n",
       "      <td>14500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>14480.000000</td>\n",
       "      <td>1514589900</td>\n",
       "      <td>14554.910759</td>\n",
       "      <td>14480.000000</td>\n",
       "      <td>14521.212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>14450.000000</td>\n",
       "      <td>1514590200</td>\n",
       "      <td>14492.085305</td>\n",
       "      <td>14415.146887</td>\n",
       "      <td>14480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>14407.433310</td>\n",
       "      <td>1514590500</td>\n",
       "      <td>14450.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>14400.000000</td>\n",
       "      <td>1514590800</td>\n",
       "      <td>14425.000000</td>\n",
       "      <td>14350.000001</td>\n",
       "      <td>14400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close   Timestamp          High           Low          Open\n",
       "0   14300.000000  1514561100  14320.000000  14170.015912  14244.748167\n",
       "1   14320.589161  1514561400  14380.494000  14258.000000  14300.000000\n",
       "2   14405.676265  1514561700  14459.160000  14320.589161  14320.589161\n",
       "3   14489.000000  1514562000  14489.000000  14360.000000  14430.242615\n",
       "4   14500.000000  1514562300  14500.000000  14469.234730  14489.168781\n",
       "5   14441.093396  1514562600  14526.800298  14410.000000  14500.000000\n",
       "6   14484.211019  1514562900  14499.000001  14345.774573  14441.093396\n",
       "7   14408.521121  1514563200  14499.000000  14260.000000  14441.982752\n",
       "8   14430.000000  1514563500  14457.000000  14320.000000  14408.521121\n",
       "9   14550.000000  1514563800  14550.000000  14405.000000  14430.000000\n",
       "10  14600.000000  1514564100  14630.833638  14503.022631  14503.022631\n",
       "11  14561.000000  1514564400  14601.000000  14550.000000  14600.000000\n",
       "12  14617.541440  1514564700  14641.112299  14529.640311  14575.000000\n",
       "13  14650.000000  1514565000  14692.557763  14617.541440  14617.541440\n",
       "14  14722.552314  1514565300  14772.397869  14650.000000  14660.172648\n",
       "15  14700.000000  1514565600  14800.000000  14679.093627  14722.552314\n",
       "16  14662.000000  1514565900  14720.000000  14626.561050  14700.000000\n",
       "17  14581.000001  1514566200  14662.000000  14508.841627  14637.113082\n",
       "18  14511.009300  1514566500  14596.000000  14469.130536  14581.000001\n",
       "19  14460.000000  1514566800  14612.000000  14421.000000  14511.009300\n",
       "20  14568.023391  1514567100  14650.000000  14460.000000  14480.000000\n",
       "21  14580.559876  1514567400  14665.000000  14550.000000  14597.559876\n",
       "22  14557.000000  1514567700  14610.000000  14550.000000  14590.000000\n",
       "23  14631.229399  1514568000  14639.000000  14557.000001  14557.000001\n",
       "24  14650.000000  1514568300  14660.000000  14597.559877  14631.229400\n",
       "25  14646.078294  1514568600  14699.000000  14600.000000  14660.000000\n",
       "26  14699.966941  1514568900  14702.509015  14616.278866  14646.078294\n",
       "27  14622.040735  1514569200  14699.966941  14600.000000  14699.966941\n",
       "28  14570.000000  1514569500  14650.899897  14515.406246  14622.040735\n",
       "29  14470.000000  1514569800  14570.000000  14431.108000  14570.000000\n",
       "..           ...         ...           ...           ...           ...\n",
       "70  14325.772990  1514582100  14403.708980  14271.000000  14317.375141\n",
       "71  14300.010000  1514582400  14374.515000  14281.655035  14325.700000\n",
       "72  14214.000000  1514582700  14300.010000  14210.000000  14300.010000\n",
       "73  14165.388898  1514583000  14241.010723  14164.388898  14210.000000\n",
       "74  14200.000000  1514583300  14238.548702  14165.388898  14183.007201\n",
       "75  14181.000000  1514583600  14256.249000  14158.120000  14200.000000\n",
       "76  14001.000001  1514583900  14200.000000  13976.498067  14181.000000\n",
       "77  14129.999999  1514584200  14172.000000  13976.237000  14001.000001\n",
       "78  14180.000000  1514584500  14200.000000  14051.437045  14129.999999\n",
       "79  14104.125000  1514584800  14206.747499  14104.125000  14180.000000\n",
       "80  14155.000000  1514585100  14198.425843  14100.541500  14165.000000\n",
       "81  14216.178247  1514585400  14282.235000  14131.000003  14155.000000\n",
       "82  14241.601278  1514585700  14282.520000  14141.000000  14201.000000\n",
       "83  14234.548783  1514586000  14289.548783  14200.020001  14241.601278\n",
       "84  14288.865950  1514586300  14329.191960  14234.558783  14270.000000\n",
       "85  14294.706193  1514586600  14320.000000  14227.567240  14319.999999\n",
       "86  14326.278692  1514586900  14332.560000  14192.721515  14294.706193\n",
       "87  14278.999900  1514587200  14326.278692  14264.090630  14326.278692\n",
       "88  14265.001000  1514587500  14313.379294  14265.001000  14278.999900\n",
       "89  14300.000001  1514587800  14340.367118  14233.644840  14265.001000\n",
       "90  14352.690000  1514588100  14365.000000  14300.000000  14300.000001\n",
       "91  14379.000000  1514588400  14382.010643  14352.690000  14352.690000\n",
       "92  14400.000000  1514588700  14400.000000  14365.000000  14378.000000\n",
       "93  14490.000001  1514589000  14500.000000  14375.000000  14400.000000\n",
       "94  14500.000000  1514589300  14525.000000  14400.000000  14465.664015\n",
       "95  14517.361256  1514589600  14560.000000  14486.374908  14500.000000\n",
       "96  14480.000000  1514589900  14554.910759  14480.000000  14521.212200\n",
       "97  14450.000000  1514590200  14492.085305  14415.146887  14480.000000\n",
       "98  14407.433310  1514590500  14450.000000  14270.000000  14450.000000\n",
       "99  14400.000000  1514590800  14425.000000  14350.000001  14400.000000\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(d)\n",
    "original_columns=[u'close', u'date', u'high', u'low', u'open']\n",
    "new_columns = ['Close','Timestamp','High','Low','Open']\n",
    "df = df.loc[:,original_columns]\n",
    "df.columns = new_columns\n",
    "df.to_csv('data/test.csv',index=None)\n",
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PastSampler import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp='data/test.csv'\n",
    "columns = ['Close']\n",
    "time_stamps = df['Timestamp']\n",
    "df=pd.read_csv(dfp)\n",
    "df = df.loc[:,columns]\n",
    "original_df = pd.read_csv(dfp).loc[:,columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "for c in columns:\n",
    "    df[c] = scaler.transform(df[c].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%%Features are channels\n",
    "A = np.array(df)[:,None,:]\n",
    "original_A = np.array(original_df)[:,None,:]\n",
    "time_stamps = np.array(time_stamps)[:,None,None]\n",
    "#%%Make samples of temporal sequences of pricing data (channel)\n",
    "NPS, NFS = 256, 16         #Number of past and future samples\n",
    "ps = PastSampler(NPS, NFS, sliding_window=False)\n",
    "datas, labels = ps.transform(A)\n",
    "input_times, output_times = ps.transform(time_stamps)\n",
    "original_inputs, original_outputs = ps.transform(original_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "step_size = datas.shape[1]\n",
    "batch_size= 8\n",
    "nb_features = datas.shape[2]\n",
    "epochs = 1\n",
    "output_size=16\n",
    "units= 50\n",
    "second_units=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(GRU(units=units, activation=None, input_shape=(step_size,nb_features),return_sequences=False))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(output_size))\n",
    "model.add(Activation('relu'))\n",
    "model.load_weights('weights/bitcoin2015to2017_close_GRU_1_tanh_relu_-32-0.00004.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 272, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(original_inputs,original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 272, 1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(input_times,output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 16)\n",
      "(48,)\n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(datas)\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "# we only care about the 0 axis, close price data\n",
    "\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "predicted_inverted.append(scaler.inverse_transform(predicted))\n",
    "print np.array(predicted_inverted).shape\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print np.array(predicted_inverted).shape\n",
    "output_times = pd.to_datetime(output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(816, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJQAAAJCCAYAAACWHZ1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5470121950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "# plt.plot(prediction_df.times,prediction_df.value, label = 'Predicted')\n",
    "for i in range(len(output_times)/16):\n",
    "    plt.plot(prediction_df.iloc[(i*16):(i+1)*16].times,prediction_df.value[(i*16):(i+1)*16], label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/test_gru.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
